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train | write | Like print(), but recognizes tensors and arrays and show
more details about them.
Example:
hl.write("My Tensor", my_tensor)
Prints:
My Tensor float32 (10, 3, 224, 224) min: 0.0 max: 1.0 | hiddenlayer/utils.py | def write(*args):
"""Like print(), but recognizes tensors and arrays and show
more details about them.
Example:
hl.write("My Tensor", my_tensor)
Prints:
My Tensor float32 (10, 3, 224, 224) min: 0.0 max: 1.0
"""
s = ""
for a in args:
# Convert tensors ... | def write(*args):
"""Like print(), but recognizes tensors and arrays and show
more details about them.
Example:
hl.write("My Tensor", my_tensor)
Prints:
My Tensor float32 (10, 3, 224, 224) min: 0.0 max: 1.0
"""
s = ""
for a in args:
# Convert tensors ... | [
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train | norm | Normalize an image to [0, 1] range. | hiddenlayer/canvas.py | def norm(image):
"""Normalize an image to [0, 1] range."""
min_value = image.min()
max_value = image.max()
if min_value == max_value:
return image - min_value
return (image - min_value) / (max_value - min_value) | def norm(image):
"""Normalize an image to [0, 1] range."""
min_value = image.min()
max_value = image.max()
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return image - min_value
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train | show_images | images: A list of images. I can be either:
- A list of Numpy arrays. Each array represents an image.
- A list of lists of Numpy arrays. In this case, the images in
the inner lists are concatentated to make one image. | hiddenlayer/canvas.py | def show_images(images, titles=None, cols=5, **kwargs):
"""
images: A list of images. I can be either:
- A list of Numpy arrays. Each array represents an image.
- A list of lists of Numpy arrays. In this case, the images in
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"""
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"""
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- A list of Numpy arrays. Each array represents an image.
- A list of lists of Numpy arrays. In this case, the images in
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train | Canvas.draw_summary | Inserts a text summary at the top that lists the number of steps and total
training time. | hiddenlayer/canvas.py | def draw_summary(self, history, title=""):
"""Inserts a text summary at the top that lists the number of steps and total
training time."""
# Generate summary string
time_str = str(history.get_total_time()).split(".")[0] # remove microseconds
summary = "Step: {} Time: {}".fo... | def draw_summary(self, history, title=""):
"""Inserts a text summary at the top that lists the number of steps and total
training time."""
# Generate summary string
time_str = str(history.get_total_time()).split(".")[0] # remove microseconds
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train | Canvas.draw_plot | metrics: One or more metrics parameters. Each represents the history
of one metric. | hiddenlayer/canvas.py | def draw_plot(self, metrics, labels=None, ylabel=""):
"""
metrics: One or more metrics parameters. Each represents the history
of one metric.
"""
metrics = metrics if isinstance(metrics, list) else [metrics]
# Loop through metrics
title = ""
for i, m i... | def draw_plot(self, metrics, labels=None, ylabel=""):
"""
metrics: One or more metrics parameters. Each represents the history
of one metric.
"""
metrics = metrics if isinstance(metrics, list) else [metrics]
# Loop through metrics
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train | Canvas.draw_image | Display a series of images at different time steps. | hiddenlayer/canvas.py | def draw_image(self, metric, limit=5):
"""Display a series of images at different time steps."""
rows = 1
cols = limit
self.ax.axis("off")
# Take the Axes gridspec and divide it into a grid
gs = matplotlib.gridspec.GridSpecFromSubplotSpec(
rows, cols, subplot_... | def draw_image(self, metric, limit=5):
"""Display a series of images at different time steps."""
rows = 1
cols = limit
self.ax.axis("off")
# Take the Axes gridspec and divide it into a grid
gs = matplotlib.gridspec.GridSpecFromSubplotSpec(
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train | Canvas.draw_hist | Draw a series of histograms of the selected keys over different
training steps. | hiddenlayer/canvas.py | def draw_hist(self, metric, title=""):
"""Draw a series of histograms of the selected keys over different
training steps.
"""
# TODO: assert isinstance(list(values.values())[0], np.ndarray)
rows = 1
cols = 1
limit = 10 # max steps to show
# We need a 3D... | def draw_hist(self, metric, title=""):
"""Draw a series of histograms of the selected keys over different
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train | CIFAR10._load | Load the data in memory.
Args:
dataset: string in ['train', 'test'] | demos/tf_cifar10.py | def _load(self, dataset='train'):
"""Load the data in memory.
Args:
dataset: string in ['train', 'test']
"""
data, labels = None, None
if dataset is 'train':
files = [os.path.join(self.cifar10_dir, 'data_batch_%d' % i) for i in range(1, 6)]
else:
... | def _load(self, dataset='train'):
"""Load the data in memory.
Args:
dataset: string in ['train', 'test']
"""
data, labels = None, None
if dataset is 'train':
files = [os.path.join(self.cifar10_dir, 'data_batch_%d' % i) for i in range(1, 6)]
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train | CIFAR10.model | Build a simple convnet (BN before ReLU).
Args:
inputs: a tensor of size [batch_size, height, width, channels]
mode: string in ['train', 'test']
Returns:
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Note:
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Step: 7015 - Epoch: 18/20 ... | demos/tf_cifar10.py | def model(self, inputs, mode='train'):
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Args:
inputs: a tensor of size [batch_size, height, width, channels]
mode: string in ['train', 'test']
Returns:
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Note:
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Args:
inputs: a tensor of size [batch_size, height, width, channels]
mode: string in ['train', 'test']
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train | CIFAR10.maybe_download_and_extract | Download and extract the tarball from Alex Krizhevsky's website. | demos/tf_cifar10.py | def maybe_download_and_extract(self):
"""Download and extract the tarball from Alex Krizhevsky's website."""
if not os.path.exists(self.cifar10_dir):
if not os.path.exists(self.data_dir):
os.makedirs(self.data_dir)
def _progress(count, block_size, total_size):
... | def maybe_download_and_extract(self):
"""Download and extract the tarball from Alex Krizhevsky's website."""
if not os.path.exists(self.cifar10_dir):
if not os.path.exists(self.data_dir):
os.makedirs(self.data_dir)
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train | dump_pytorch_graph | List all the nodes in a PyTorch graph. | hiddenlayer/pytorch_builder.py | def dump_pytorch_graph(graph):
"""List all the nodes in a PyTorch graph."""
f = "{:25} {:40} {} -> {}"
print(f.format("kind", "scopeName", "inputs", "outputs"))
for node in graph.nodes():
print(f.format(node.kind(), node.scopeName(),
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... | def dump_pytorch_graph(graph):
"""List all the nodes in a PyTorch graph."""
f = "{:25} {:40} {} -> {}"
print(f.format("kind", "scopeName", "inputs", "outputs"))
for node in graph.nodes():
print(f.format(node.kind(), node.scopeName(),
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train | pytorch_id | Returns a unique ID for a node. | hiddenlayer/pytorch_builder.py | def pytorch_id(node):
"""Returns a unique ID for a node."""
# After ONNX simplification, the scopeName is not unique anymore
# so append node outputs to guarantee uniqueness
return node.scopeName() + "/outputs/" + "/".join([o.uniqueName() for o in node.outputs()]) | def pytorch_id(node):
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train | get_shape | Return the output shape of the given Pytorch node. | hiddenlayer/pytorch_builder.py | def get_shape(torch_node):
"""Return the output shape of the given Pytorch node."""
# Extract node output shape from the node string representation
# This is a hack because there doesn't seem to be an official way to do it.
# See my quesiton in the PyTorch forum:
# https://discuss.pytorch.org/t/node... | def get_shape(torch_node):
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train | dump_tf_graph | List all the nodes in a TF graph.
tfgraph: A TF Graph object.
tfgraphdef: A TF GraphDef object. | hiddenlayer/tf_builder.py | def dump_tf_graph(tfgraph, tfgraphdef):
"""List all the nodes in a TF graph.
tfgraph: A TF Graph object.
tfgraphdef: A TF GraphDef object.
"""
print("Nodes ({})".format(len(tfgraphdef.node)))
f = "{:15} {:59} {:20} {}"
print(f.format("kind", "scopeName", "shape", "inputs"))
for node in t... | def dump_tf_graph(tfgraph, tfgraphdef):
"""List all the nodes in a TF graph.
tfgraph: A TF Graph object.
tfgraphdef: A TF GraphDef object.
"""
print("Nodes ({})".format(len(tfgraphdef.node)))
f = "{:15} {:59} {:20} {}"
print(f.format("kind", "scopeName", "shape", "inputs"))
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train | import_graph | Convert TF graph to directed graph
tfgraph: A TF Graph object.
output: Name of the output node (string).
verbose: Set to True for debug print output | hiddenlayer/tf_builder.py | def import_graph(hl_graph, tf_graph, output=None, verbose=False):
"""Convert TF graph to directed graph
tfgraph: A TF Graph object.
output: Name of the output node (string).
verbose: Set to True for debug print output
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# Get clean(er) list of nodes
graph_def = tf_graph.as_graph_def(add_sh... | def import_graph(hl_graph, tf_graph, output=None, verbose=False):
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partitions (set of TopicPartitions): the partitions to reset
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train | Fetcher._reset_offset | Reset offsets for the given partition using the offset reset strategy.
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partition (TopicPartition): the partition that needs reset offset
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partition (TopicPartition): the partition that needs reset offset
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train | Fetcher._retrieve_offsets | Fetch offset for each partition passed in ``timestamps`` map.
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timestamps: {TopicPartition: int} dict with timestamps to fetch
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train | Fetcher.fetched_records | Returns previously fetched records and updates consumed offsets.
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max_records (int): Maximum number of records returned. Defaults
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Raises:
OffsetOutOfRangeError: if no subscription offset_reset_strategy
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train | Fetcher._message_generator | Iterate over fetched_records | kafka/consumer/fetcher.py | def _message_generator(self):
"""Iterate over fetched_records"""
while self._next_partition_records or self._completed_fetches:
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completion = self._completed_fetches.popleft()
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Arguments:
timestamps (dict): {TopicPartition: int} mapping of fetching
timestamps.
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timestamps (dict): {TopicPartition: int} mapping of fetching
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"""Fetch offsets for each partition in timestamps dict. This may send
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timestamps (dict): {TopicPartition: int} mapping of fetching
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train | Fetcher._handle_offset_response | Callback for the response of the list offset call above.
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future (Future): the future to update based on response
response (OffsetResponse): response from the server
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AssertionError: if response does not match partition | kafka/consumer/fetcher.py | def _handle_offset_response(self, future, response):
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future (Future): the future to update based on response
response (OffsetResponse): response from the server
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Returns:
dict: {node_id: FetchRequest, ...} (version depends on api_version) | kafka/consumer/fetcher.py | def _create_fetch_requests(self):
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FetchRequests skipped if no leader, or node has requests in flight
Returns:
dict: {node_id: FetchRequest, ...} (version depends on api_version)
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"""Create fetch requests for all assigned partitions, grouped by node.
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train | Fetcher._handle_fetch_response | The callback for fetch completion | kafka/consumer/fetcher.py | def _handle_fetch_response(self, request, send_time, response):
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fetch_offsets = {}
for topic, partitions in request.topics:
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partition, offset = partition_data[:2]
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"""
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train | Consumer._auto_commit | Check if we have to commit based on number of messages and commit | kafka/consumer/base.py | def _auto_commit(self):
"""
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"""
# Check if we are supposed to do an auto-commit
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train | Consumer.pending | Gets the pending message count
Keyword Arguments:
partitions (list): list of partitions to check for, default is to check all | kafka/consumer/base.py | def pending(self, partitions=None):
"""
Gets the pending message count
Keyword Arguments:
partitions (list): list of partitions to check for, default is to check all
"""
if partitions is None:
partitions = self.offsets.keys()
total = 0
re... | def pending(self, partitions=None):
"""
Gets the pending message count
Keyword Arguments:
partitions (list): list of partitions to check for, default is to check all
"""
if partitions is None:
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train | murmur2 | Pure-python Murmur2 implementation.
Based on java client, see org.apache.kafka.common.utils.Utils.murmur2
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data (bytes): opaque bytes
Returns: MurmurHash2 of data | kafka/partitioner/hashed.py | def murmur2(data):
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data (bytes): opaque bytes
Returns: MurmurHash2 of data
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data (bytes): opaque bytes
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.. code:: python
consumer.provide_partition_info() | kafka/context.py | def mark(self, partition, offset):
"""
Set the high-water mark in the current context.
In order to know the current partition, it is helpful to initialize
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.. code:: python
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.. code:: python
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train | OffsetCommitContext.rollback | Rollback this context:
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"""
Rollback this context:
- Position the consumer at the initial offsets.
"""
self.logger.info("Rolling back context: %s", self.initial_offsets)
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train | OffsetCommitContext.commit_partition_offsets | Commit explicit partition/offset pairs. | kafka/context.py | def commit_partition_offsets(self, partition_offsets):
"""
Commit explicit partition/offset pairs.
"""
self.logger.debug("Committing partition offsets: %s", partition_offsets)
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Commit explicit partition/offset pairs.
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train | OffsetCommitContext.update_consumer_offsets | Update consumer offsets to explicit positions. | kafka/context.py | def update_consumer_offsets(self, partition_offsets):
"""
Update consumer offsets to explicit positions.
"""
self.logger.debug("Updating consumer offsets to: %s", partition_offsets)
for partition, offset in partition_offsets.items():
self.consumer.offsets[partition] ... | def update_consumer_offsets(self, partition_offsets):
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train | BaseCoordinator.coordinator | Get the current coordinator
Returns: the current coordinator id or None if it is unknown | kafka/coordinator/base.py | def coordinator(self):
"""Get the current coordinator
Returns: the current coordinator id or None if it is unknown
"""
if self.coordinator_id is None:
return None
elif self._client.is_disconnected(self.coordinator_id):
self.coordinator_dead('Node Disconne... | def coordinator(self):
"""Get the current coordinator
Returns: the current coordinator id or None if it is unknown
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train | BaseCoordinator.ensure_coordinator_ready | Block until the coordinator for this group is known
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"""Block until the coordinator for this group is known
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"""
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train | BaseCoordinator.poll_heartbeat | Check the status of the heartbeat thread (if it is active) and indicate
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joining with :meth:`.ensure_active_group` to ensure that the member stays
in the group. If an interval of time longer than the provided rebalance
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Check the status of the heartbeat thread (if it is active) and indicate
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Check the status of the heartbeat thread (if it is active) and indicate
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train | BaseCoordinator.ensure_active_group | Ensure that the group is active (i.e. joined and synced) | kafka/coordinator/base.py | def ensure_active_group(self):
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train | BaseCoordinator._send_join_group_request | Join the group and return the assignment for the next generation.
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Returns:
Future: resolves to the encoded-bytes assignment returned from the
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Future: resolves to the enc... | def _send_join_group_request(self):
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response (JoinResponse): broker response to parse
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Future: resolves to member assignment encoded-bytes | kafka/coordinator/base.py | def _on_join_leader(self, response):
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Perform leader synchronization and send back the assignment
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train | BaseCoordinator._send_group_coordinator_request | Discover the current coordinator for the group.
Returns:
Future: resolves to the node id of the coordinator | kafka/coordinator/base.py | def _send_group_coordinator_request(self):
"""Discover the current coordinator for the group.
Returns:
Future: resolves to the node id of the coordinator
"""
node_id = self._client.least_loaded_node()
if node_id is None:
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train | BaseCoordinator.coordinator_dead | Mark the current coordinator as dead. | kafka/coordinator/base.py | def coordinator_dead(self, error):
"""Mark the current coordinator as dead."""
if self.coordinator_id is not None:
log.warning("Marking the coordinator dead (node %s) for group %s: %s.",
self.coordinator_id, self.group_id, error)
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train | BaseCoordinator.generation | Get the current generation state if the group is stable.
Returns: the current generation or None if the group is unjoined/rebalancing | kafka/coordinator/base.py | def generation(self):
"""Get the current generation state if the group is stable.
Returns: the current generation or None if the group is unjoined/rebalancing
"""
with self._lock:
if self.state is not MemberState.STABLE:
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train | BaseCoordinator.reset_generation | Reset the generation and memberId because we have fallen out of the group. | kafka/coordinator/base.py | def reset_generation(self):
"""Reset the generation and memberId because we have fallen out of the group."""
with self._lock:
self._generation = Generation.NO_GENERATION
self.rejoin_needed = True
self.state = MemberState.UNJOINED | def reset_generation(self):
"""Reset the generation and memberId because we have fallen out of the group."""
with self._lock:
self._generation = Generation.NO_GENERATION
self.rejoin_needed = True
self.state = MemberState.UNJOINED | [
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train | BaseCoordinator.maybe_leave_group | Leave the current group and reset local generation/memberId. | kafka/coordinator/base.py | def maybe_leave_group(self):
"""Leave the current group and reset local generation/memberId."""
with self._client._lock, self._lock:
if (not self.coordinator_unknown()
and self.state is not MemberState.UNJOINED
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train | BaseCoordinator._send_heartbeat_request | Send a heartbeat request | kafka/coordinator/base.py | def _send_heartbeat_request(self):
"""Send a heartbeat request"""
if self.coordinator_unknown():
e = Errors.GroupCoordinatorNotAvailableError(self.coordinator_id)
return Future().failure(e)
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return Future().failure(e)
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train | Metrics.metric_name | Create a MetricName with the given name, group, description and tags,
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Tag in tags takes precedence if the same tag key is specified in
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Arguments:
name (str): The name of the metric
... | kafka/metrics/metrics.py | def metric_name(self, name, group, description='', tags=None):
"""
Create a MetricName with the given name, group, description and tags,
plus default tags specified in the metric configuration.
Tag in tags takes precedence if the same tag key is specified in
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Create a MetricName with the given name, group, description and tags,
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train | Metrics.sensor | Get or create a sensor with the given unique name and zero or
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Arguments:
name (str): The name of the sensor
config (MetricConfig, optional): A default configuration to use
... | kafka/metrics/metrics.py | def sensor(self, name, config=None,
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parents=None):
"""
Get or create a sensor with the given unique name and zero or
more parent sensors. All parent sensors will receive every value
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train | Metrics.remove_sensor | Remove a sensor (if it exists), associated metrics and its children.
Arguments:
name (str): The name of the sensor to be removed | kafka/metrics/metrics.py | def remove_sensor(self, name):
"""
Remove a sensor (if it exists), associated metrics and its children.
Arguments:
name (str): The name of the sensor to be removed
"""
sensor = self._sensors.get(name)
if sensor:
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wi... | def remove_sensor(self, name):
"""
Remove a sensor (if it exists), associated metrics and its children.
Arguments:
name (str): The name of the sensor to be removed
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sensor = self._sensors.get(name)
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train | Metrics.add_metric | Add a metric to monitor an object that implements measurable.
This metric won't be associated with any sensor.
This is a way to expose existing values as metrics.
Arguments:
metricName (MetricName): The name of the metric
measurable (AbstractMeasurable): The measurable t... | kafka/metrics/metrics.py | def add_metric(self, metric_name, measurable, config=None):
"""
Add a metric to monitor an object that implements measurable.
This metric won't be associated with any sensor.
This is a way to expose existing values as metrics.
Arguments:
metricName (MetricName): The ... | def add_metric(self, metric_name, measurable, config=None):
"""
Add a metric to monitor an object that implements measurable.
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This is a way to expose existing values as metrics.
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train | Metrics.remove_metric | Remove a metric if it exists and return it. Return None otherwise.
If a metric is removed, `metric_removal` will be invoked
for each reporter.
Arguments:
metric_name (MetricName): The name of the metric
Returns:
KafkaMetric: the removed `KafkaMetric` or None if ... | kafka/metrics/metrics.py | def remove_metric(self, metric_name):
"""
Remove a metric if it exists and return it. Return None otherwise.
If a metric is removed, `metric_removal` will be invoked
for each reporter.
Arguments:
metric_name (MetricName): The name of the metric
Returns:
... | def remove_metric(self, metric_name):
"""
Remove a metric if it exists and return it. Return None otherwise.
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Arguments:
metric_name (MetricName): The name of the metric
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train | Metrics.add_reporter | Add a MetricReporter | kafka/metrics/metrics.py | def add_reporter(self, reporter):
"""Add a MetricReporter"""
with self._lock:
reporter.init(list(self.metrics.values()))
self._reporters.append(reporter) | def add_reporter(self, reporter):
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reporter.init(list(self.metrics.values()))
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train | Metrics.close | Close this metrics repository. | kafka/metrics/metrics.py | def close(self):
"""Close this metrics repository."""
for reporter in self._reporters:
reporter.close()
self._metrics.clear() | def close(self):
"""Close this metrics repository."""
for reporter in self._reporters:
reporter.close()
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train | create_snappy_message | Construct a Snappy Message containing multiple Messages
The given payloads will be encoded, compressed, and sent as a single atomic
message to Kafka.
Arguments:
payloads: list(bytes), a list of payload to send be sent to Kafka
key: bytes, a key used for partition routing (optional) | kafka/protocol/legacy.py | def create_snappy_message(payloads, key=None):
"""
Construct a Snappy Message containing multiple Messages
The given payloads will be encoded, compressed, and sent as a single atomic
message to Kafka.
Arguments:
payloads: list(bytes), a list of payload to send be sent to Kafka
key:... | def create_snappy_message(payloads, key=None):
"""
Construct a Snappy Message containing multiple Messages
The given payloads will be encoded, compressed, and sent as a single atomic
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Arguments:
payloads: list(bytes), a list of payload to send be sent to Kafka
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train | create_message_set | Create a message set using the given codec.
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return a list containing a single codec-encoded message. | kafka/protocol/legacy.py | def create_message_set(messages, codec=CODEC_NONE, key=None, compresslevel=None):
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train | KafkaProtocol._encode_message_header | Encode the common request envelope | kafka/protocol/legacy.py | def _encode_message_header(cls, client_id, correlation_id, request_key,
version=0):
"""
Encode the common request envelope
"""
return struct.pack('>hhih%ds' % len(client_id),
request_key, # ApiKey
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"""
Encode the common request envelope
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return struct.pack('>hhih%ds' % len(client_id),
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train | KafkaProtocol._encode_message_set | Encode a MessageSet. Unlike other arrays in the protocol,
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Format
======
MessageSet => [Offset MessageSize Message]
Offset => int64
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"""
Encode a MessageSet. Unlike other arrays in the protocol,
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Format
======
MessageSet => [Offset MessageSize Message]
Offset => int64
MessageSize => int32
"""
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"""
Encode a MessageSet. Unlike other arrays in the protocol,
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MessageSet => [Offset MessageSize Message]
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train | KafkaProtocol.encode_produce_request | Encode a ProduceRequest struct
Arguments:
payloads: list of ProduceRequestPayload
acks: How "acky" you want the request to be
1: written to disk by the leader
0: immediate response
-1: waits for all replicas to be in sync
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"""
Encode a ProduceRequest struct
Arguments:
payloads: list of ProduceRequestPayload
acks: How "acky" you want the request to be
1: written to disk by the leader
0: immed... | def encode_produce_request(cls, payloads=(), acks=1, timeout=1000):
"""
Encode a ProduceRequest struct
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payloads: list of ProduceRequestPayload
acks: How "acky" you want the request to be
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train | KafkaProtocol.decode_produce_response | Decode ProduceResponse to ProduceResponsePayload
Arguments:
response: ProduceResponse
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Decode ProduceResponse to ProduceResponsePayload
Arguments:
response: ProduceResponse
Return: list of ProduceResponsePayload
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Decode ProduceResponse to ProduceResponsePayload
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train | KafkaProtocol.decode_fetch_response | Decode FetchResponse struct to FetchResponsePayloads
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Decode FetchResponse struct to FetchResponsePayloads
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train | KafkaProtocol.decode_offset_response | Decode OffsetResponse into OffsetResponsePayloads
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Decode OffsetResponse into OffsetResponsePayloads
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Returns: list of OffsetResponsePayloads
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train | KafkaProtocol.decode_list_offset_response | Decode OffsetResponse_v2 into ListOffsetResponsePayloads
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Decode OffsetResponse_v2 into ListOffsetResponsePayloads
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train | KafkaProtocol.encode_metadata_request | Encode a MetadataRequest
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"""
Encode a MetadataRequest
Arguments:
topics: list of strings
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topics = payloads
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train | KafkaProtocol.encode_consumer_metadata_request | Encode a ConsumerMetadataRequest
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payloads: string (consumer group)
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train | KafkaProtocol.decode_consumer_metadata_response | Decode bytes to a kafka.structs.ConsumerMetadataResponse
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"""
Decode bytes to a kafka.structs.ConsumerMetadataResponse
Arguments:
data: bytes to decode
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train | KafkaProtocol.encode_offset_commit_request | Encode an OffsetCommitRequest struct
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group: string, the consumer group you are committing offsets for
payloads: list of OffsetCommitRequestPayload | kafka/protocol/legacy.py | def encode_offset_commit_request(cls, group, payloads):
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Encode an OffsetCommitRequest struct
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group: string, the consumer group you are committing offsets for
payloads: list of OffsetCommitRequestPayload
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train | KafkaProtocol.decode_offset_commit_response | Decode OffsetCommitResponse to an OffsetCommitResponsePayload
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"""
Decode OffsetCommitResponse to an OffsetCommitResponsePayload
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Decode OffsetFetchResponse to OffsetFetchResponsePayloads
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train | Sensor._check_forest | Validate that this sensor doesn't end up referencing itself. | kafka/metrics/stats/sensor.py | def _check_forest(self, sensors):
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value (double): The value we are recording
time_ms (int): A POSIX timestamp in milliseconds.
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Raises:
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value (double): The value we are recording
time_ms (int): A POSIX timestamp in milliseconds.
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train | Sensor._check_quotas | Check if we have violated our quota for any metric that
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"""
Check if we have violated our quota for any metric that
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"""
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train | Sensor.add_compound | Register a compound statistic with this sensor which
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Arguments:
stat (AbstractCompoundStat): The stat to register
config (MetricConfig): The configuration for this stat.
If None then the stat will use the d... | kafka/metrics/stats/sensor.py | def add_compound(self, compound_stat, config=None):
"""
Register a compound statistic with this sensor which
yields multiple measurable quantities (like a histogram)
Arguments:
stat (AbstractCompoundStat): The stat to register
config (MetricConfig): The configura... | def add_compound(self, compound_stat, config=None):
"""
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stat (AbstractCompoundStat): The stat to register
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train | Sensor.add | Register a metric with this sensor
Arguments:
metric_name (MetricName): The name of the metric
stat (AbstractMeasurableStat): The statistic to keep
config (MetricConfig): A special configuration for this metric.
If None use the sensor default configuration. | kafka/metrics/stats/sensor.py | def add(self, metric_name, stat, config=None):
"""
Register a metric with this sensor
Arguments:
metric_name (MetricName): The name of the metric
stat (AbstractMeasurableStat): The statistic to keep
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Register a metric with this sensor
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train | ConsumerCoordinator.group_protocols | Returns list of preferred (protocols, metadata) | kafka/coordinator/consumer.py | def group_protocols(self):
"""Returns list of preferred (protocols, metadata)"""
if self._subscription.subscription is None:
raise Errors.IllegalStateError('Consumer has not subscribed to topics')
# dpkp note: I really dislike this.
# why? because we are using this strange me... | def group_protocols(self):
"""Returns list of preferred (protocols, metadata)"""
if self._subscription.subscription is None:
raise Errors.IllegalStateError('Consumer has not subscribed to topics')
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train | ConsumerCoordinator.poll | Poll for coordinator events. Only applicable if group_id is set, and
broker version supports GroupCoordinators. This ensures that the
coordinator is known, and if using automatic partition assignment,
ensures that the consumer has joined the group. This also handles
periodic offset commi... | kafka/coordinator/consumer.py | def poll(self):
"""
Poll for coordinator events. Only applicable if group_id is set, and
broker version supports GroupCoordinators. This ensures that the
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"""
Poll for coordinator events. Only applicable if group_id is set, and
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train | ConsumerCoordinator.time_to_next_poll | Return seconds (float) remaining until :meth:`.poll` should be called again | kafka/coordinator/consumer.py | def time_to_next_poll(self):
"""Return seconds (float) remaining until :meth:`.poll` should be called again"""
if not self.config['enable_auto_commit']:
return self.time_to_next_heartbeat()
if time.time() > self.next_auto_commit_deadline:
return 0
return min(sel... | def time_to_next_poll(self):
"""Return seconds (float) remaining until :meth:`.poll` should be called again"""
if not self.config['enable_auto_commit']:
return self.time_to_next_heartbeat()
if time.time() > self.next_auto_commit_deadline:
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train | ConsumerCoordinator.need_rejoin | Check whether the group should be rejoined
Returns:
bool: True if consumer should rejoin group, False otherwise | kafka/coordinator/consumer.py | def need_rejoin(self):
"""Check whether the group should be rejoined
Returns:
bool: True if consumer should rejoin group, False otherwise
"""
if not self._subscription.partitions_auto_assigned():
return False
if self._auto_assign_all_partitions():
... | def need_rejoin(self):
"""Check whether the group should be rejoined
Returns:
bool: True if consumer should rejoin group, False otherwise
"""
if not self._subscription.partitions_auto_assigned():
return False
if self._auto_assign_all_partitions():
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train | ConsumerCoordinator.refresh_committed_offsets_if_needed | Fetch committed offsets for assigned partitions. | kafka/coordinator/consumer.py | def refresh_committed_offsets_if_needed(self):
"""Fetch committed offsets for assigned partitions."""
if self._subscription.needs_fetch_committed_offsets:
offsets = self.fetch_committed_offsets(self._subscription.assigned_partitions())
for partition, offset in six.iteritems(offse... | def refresh_committed_offsets_if_needed(self):
"""Fetch committed offsets for assigned partitions."""
if self._subscription.needs_fetch_committed_offsets:
offsets = self.fetch_committed_offsets(self._subscription.assigned_partitions())
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train | ConsumerCoordinator.fetch_committed_offsets | Fetch the current committed offsets for specified partitions
Arguments:
partitions (list of TopicPartition): partitions to fetch
Returns:
dict: {TopicPartition: OffsetAndMetadata} | kafka/coordinator/consumer.py | def fetch_committed_offsets(self, partitions):
"""Fetch the current committed offsets for specified partitions
Arguments:
partitions (list of TopicPartition): partitions to fetch
Returns:
dict: {TopicPartition: OffsetAndMetadata}
"""
if not partitions:
... | def fetch_committed_offsets(self, partitions):
"""Fetch the current committed offsets for specified partitions
Arguments:
partitions (list of TopicPartition): partitions to fetch
Returns:
dict: {TopicPartition: OffsetAndMetadata}
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train | ConsumerCoordinator.close | Close the coordinator, leave the current group,
and reset local generation / member_id.
Keyword Arguments:
autocommit (bool): If auto-commit is configured for this consumer,
this optional flag causes the consumer to attempt to commit any
pending consumed offs... | kafka/coordinator/consumer.py | def close(self, autocommit=True):
"""Close the coordinator, leave the current group,
and reset local generation / member_id.
Keyword Arguments:
autocommit (bool): If auto-commit is configured for this consumer,
this optional flag causes the consumer to attempt to com... | def close(self, autocommit=True):
"""Close the coordinator, leave the current group,
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Keyword Arguments:
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train | ConsumerCoordinator.commit_offsets_async | Commit specific offsets asynchronously.
Arguments:
offsets (dict {TopicPartition: OffsetAndMetadata}): what to commit
callback (callable, optional): called as callback(offsets, response)
response will be either an Exception or a OffsetCommitResponse
struc... | kafka/coordinator/consumer.py | def commit_offsets_async(self, offsets, callback=None):
"""Commit specific offsets asynchronously.
Arguments:
offsets (dict {TopicPartition: OffsetAndMetadata}): what to commit
callback (callable, optional): called as callback(offsets, response)
response will be ... | def commit_offsets_async(self, offsets, callback=None):
"""Commit specific offsets asynchronously.
Arguments:
offsets (dict {TopicPartition: OffsetAndMetadata}): what to commit
callback (callable, optional): called as callback(offsets, response)
response will be ... | [
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train | ConsumerCoordinator.commit_offsets_sync | Commit specific offsets synchronously.
This method will retry until the commit completes successfully or an
unrecoverable error is encountered.
Arguments:
offsets (dict {TopicPartition: OffsetAndMetadata}): what to commit
Raises error on failure | kafka/coordinator/consumer.py | def commit_offsets_sync(self, offsets):
"""Commit specific offsets synchronously.
This method will retry until the commit completes successfully or an
unrecoverable error is encountered.
Arguments:
offsets (dict {TopicPartition: OffsetAndMetadata}): what to commit
... | def commit_offsets_sync(self, offsets):
"""Commit specific offsets synchronously.
This method will retry until the commit completes successfully or an
unrecoverable error is encountered.
Arguments:
offsets (dict {TopicPartition: OffsetAndMetadata}): what to commit
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train | ConsumerCoordinator._send_offset_commit_request | Commit offsets for the specified list of topics and partitions.
This is a non-blocking call which returns a request future that can be
polled in the case of a synchronous commit or ignored in the
asynchronous case.
Arguments:
offsets (dict of {TopicPartition: OffsetAndMetad... | kafka/coordinator/consumer.py | def _send_offset_commit_request(self, offsets):
"""Commit offsets for the specified list of topics and partitions.
This is a non-blocking call which returns a request future that can be
polled in the case of a synchronous commit or ignored in the
asynchronous case.
Arguments:
... | def _send_offset_commit_request(self, offsets):
"""Commit offsets for the specified list of topics and partitions.
This is a non-blocking call which returns a request future that can be
polled in the case of a synchronous commit or ignored in the
asynchronous case.
Arguments:
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train | ConsumerCoordinator._send_offset_fetch_request | Fetch the committed offsets for a set of partitions.
This is a non-blocking call. The returned future can be polled to get
the actual offsets returned from the broker.
Arguments:
partitions (list of TopicPartition): the partitions to fetch
Returns:
Future: reso... | kafka/coordinator/consumer.py | def _send_offset_fetch_request(self, partitions):
"""Fetch the committed offsets for a set of partitions.
This is a non-blocking call. The returned future can be polled to get
the actual offsets returned from the broker.
Arguments:
partitions (list of TopicPartition): the p... | def _send_offset_fetch_request(self, partitions):
"""Fetch the committed offsets for a set of partitions.
This is a non-blocking call. The returned future can be polled to get
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train | SubscriptionState.subscribe | Subscribe to a list of topics, or a topic regex pattern.
Partitions will be dynamically assigned via a group coordinator.
Topic subscriptions are not incremental: this list will replace the
current assignment (if there is one).
This method is incompatible with assign_from_user()
... | kafka/consumer/subscription_state.py | def subscribe(self, topics=(), pattern=None, listener=None):
"""Subscribe to a list of topics, or a topic regex pattern.
Partitions will be dynamically assigned via a group coordinator.
Topic subscriptions are not incremental: this list will replace the
current assignment (if there is o... | def subscribe(self, topics=(), pattern=None, listener=None):
"""Subscribe to a list of topics, or a topic regex pattern.
Partitions will be dynamically assigned via a group coordinator.
Topic subscriptions are not incremental: this list will replace the
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train | SubscriptionState._ensure_valid_topic_name | Ensures that the topic name is valid according to the kafka source. | kafka/consumer/subscription_state.py | def _ensure_valid_topic_name(self, topic):
""" Ensures that the topic name is valid according to the kafka source. """
# See Kafka Source:
# https://github.com/apache/kafka/blob/39eb31feaeebfb184d98cc5d94da9148c2319d81/clients/src/main/java/org/apache/kafka/common/internals/Topic.java
i... | def _ensure_valid_topic_name(self, topic):
""" Ensures that the topic name is valid according to the kafka source. """
# See Kafka Source:
# https://github.com/apache/kafka/blob/39eb31feaeebfb184d98cc5d94da9148c2319d81/clients/src/main/java/org/apache/kafka/common/internals/Topic.java
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train | SubscriptionState.change_subscription | Change the topic subscription.
Arguments:
topics (list of str): topics for subscription
Raises:
IllegalStateErrror: if assign_from_user has been used already
TypeError: if a topic is None or a non-str
ValueError: if a topic is an empty string or
... | kafka/consumer/subscription_state.py | def change_subscription(self, topics):
"""Change the topic subscription.
Arguments:
topics (list of str): topics for subscription
Raises:
IllegalStateErrror: if assign_from_user has been used already
TypeError: if a topic is None or a non-str
Val... | def change_subscription(self, topics):
"""Change the topic subscription.
Arguments:
topics (list of str): topics for subscription
Raises:
IllegalStateErrror: if assign_from_user has been used already
TypeError: if a topic is None or a non-str
Val... | [
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train | SubscriptionState.group_subscribe | Add topics to the current group subscription.
This is used by the group leader to ensure that it receives metadata
updates for all topics that any member of the group is subscribed to.
Arguments:
topics (list of str): topics to add to the group subscription | kafka/consumer/subscription_state.py | def group_subscribe(self, topics):
"""Add topics to the current group subscription.
This is used by the group leader to ensure that it receives metadata
updates for all topics that any member of the group is subscribed to.
Arguments:
topics (list of str): topics to add to t... | def group_subscribe(self, topics):
"""Add topics to the current group subscription.
This is used by the group leader to ensure that it receives metadata
updates for all topics that any member of the group is subscribed to.
Arguments:
topics (list of str): topics to add to t... | [
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train | SubscriptionState.reset_group_subscription | Reset the group's subscription to only contain topics subscribed by this consumer. | kafka/consumer/subscription_state.py | def reset_group_subscription(self):
"""Reset the group's subscription to only contain topics subscribed by this consumer."""
if self._user_assignment:
raise IllegalStateError(self._SUBSCRIPTION_EXCEPTION_MESSAGE)
assert self.subscription is not None, 'Subscription required'
s... | def reset_group_subscription(self):
"""Reset the group's subscription to only contain topics subscribed by this consumer."""
if self._user_assignment:
raise IllegalStateError(self._SUBSCRIPTION_EXCEPTION_MESSAGE)
assert self.subscription is not None, 'Subscription required'
s... | [
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train | SubscriptionState.assign_from_user | Manually assign a list of TopicPartitions to this consumer.
This interface does not allow for incremental assignment and will
replace the previous assignment (if there was one).
Manual topic assignment through this method does not use the consumer's
group management functionality. As s... | kafka/consumer/subscription_state.py | def assign_from_user(self, partitions):
"""Manually assign a list of TopicPartitions to this consumer.
This interface does not allow for incremental assignment and will
replace the previous assignment (if there was one).
Manual topic assignment through this method does not use the cons... | def assign_from_user(self, partitions):
"""Manually assign a list of TopicPartitions to this consumer.
This interface does not allow for incremental assignment and will
replace the previous assignment (if there was one).
Manual topic assignment through this method does not use the cons... | [
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train | SubscriptionState.assign_from_subscribed | Update the assignment to the specified partitions
This method is called by the coordinator to dynamically assign
partitions based on the consumer's topic subscription. This is different
from assign_from_user() which directly sets the assignment from a
user-supplied TopicPartition list.
... | kafka/consumer/subscription_state.py | def assign_from_subscribed(self, assignments):
"""Update the assignment to the specified partitions
This method is called by the coordinator to dynamically assign
partitions based on the consumer's topic subscription. This is different
from assign_from_user() which directly sets the ass... | def assign_from_subscribed(self, assignments):
"""Update the assignment to the specified partitions
This method is called by the coordinator to dynamically assign
partitions based on the consumer's topic subscription. This is different
from assign_from_user() which directly sets the ass... | [
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] | dpkp/kafka-python | python | https://github.com/dpkp/kafka-python/blob/f6a8a38937688ea2cc5dc13d3d1039493be5c9b5/kafka/consumer/subscription_state.py#L233-L257 | [
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train | SubscriptionState.unsubscribe | Clear all topic subscriptions and partition assignments | kafka/consumer/subscription_state.py | def unsubscribe(self):
"""Clear all topic subscriptions and partition assignments"""
self.subscription = None
self._user_assignment.clear()
self.assignment.clear()
self.subscribed_pattern = None | def unsubscribe(self):
"""Clear all topic subscriptions and partition assignments"""
self.subscription = None
self._user_assignment.clear()
self.assignment.clear()
self.subscribed_pattern = None | [
"Clear",
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] | dpkp/kafka-python | python | https://github.com/dpkp/kafka-python/blob/f6a8a38937688ea2cc5dc13d3d1039493be5c9b5/kafka/consumer/subscription_state.py#L259-L264 | [
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train | SubscriptionState.paused_partitions | Return current set of paused TopicPartitions. | kafka/consumer/subscription_state.py | def paused_partitions(self):
"""Return current set of paused TopicPartitions."""
return set(partition for partition in self.assignment
if self.is_paused(partition)) | def paused_partitions(self):
"""Return current set of paused TopicPartitions."""
return set(partition for partition in self.assignment
if self.is_paused(partition)) | [
"Return",
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] | dpkp/kafka-python | python | https://github.com/dpkp/kafka-python/blob/f6a8a38937688ea2cc5dc13d3d1039493be5c9b5/kafka/consumer/subscription_state.py#L300-L303 | [
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train | SubscriptionState.fetchable_partitions | Return set of TopicPartitions that should be Fetched. | kafka/consumer/subscription_state.py | def fetchable_partitions(self):
"""Return set of TopicPartitions that should be Fetched."""
fetchable = set()
for partition, state in six.iteritems(self.assignment):
if state.is_fetchable():
fetchable.add(partition)
return fetchable | def fetchable_partitions(self):
"""Return set of TopicPartitions that should be Fetched."""
fetchable = set()
for partition, state in six.iteritems(self.assignment):
if state.is_fetchable():
fetchable.add(partition)
return fetchable | [
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train | SubscriptionState.all_consumed_offsets | Returns consumed offsets as {TopicPartition: OffsetAndMetadata} | kafka/consumer/subscription_state.py | def all_consumed_offsets(self):
"""Returns consumed offsets as {TopicPartition: OffsetAndMetadata}"""
all_consumed = {}
for partition, state in six.iteritems(self.assignment):
if state.has_valid_position:
all_consumed[partition] = OffsetAndMetadata(state.position, '')... | def all_consumed_offsets(self):
"""Returns consumed offsets as {TopicPartition: OffsetAndMetadata}"""
all_consumed = {}
for partition, state in six.iteritems(self.assignment):
if state.has_valid_position:
all_consumed[partition] = OffsetAndMetadata(state.position, '')... | [
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"... | f6a8a38937688ea2cc5dc13d3d1039493be5c9b5 |
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